Collaborative Filtering (CF) is a technique used by some recommender systems to improve recommendations. In general, CF is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. CF has applications in both information filtering and E-commerce. For example, many web-sites, such as Amazon and Netflix, use CF in order to provide recommendations to users.
Recommender systems that incorporate CF can generate more personalized recommendations by analyzing the activity of other users with a similar taste, characteristics, viewpoints, etc., to the active user. As used herein, an active user is a user for which it is desired to predict preferences. For example, an active user may be a user shopping on an E-commerce website or browsing videos on an online video website.
An underlying assumption of the CF approach is that similar users have similar preferences. In other words, by examining the preferences of users that are in some sense similar to the active user, the recommender system can predict a ranked list of items which the active user will most probably like. By using CF, a recommender system may be able to make recommendations to an active user that are more likely to be interesting to the active user, and thus, accepted by the active user.